ibm_the mdm advantage

8/12/2019 IBM_The MDM Advantage

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IBM Software

The MDM advantage:Creating insight from big data


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Introduction The importanceof understanding

your customers

How MDMenhances bigdataandvice versa

Leveragingbig data withIBM InfoSphereMDM

Unlock thevalue ofbig data

Why InfoSphere?

The MDM advantage: Creating insight from big data

1 2 3 4 5 6


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1 Introduction 2 The importance ofunderstanding yourcustomers

3 How MDM enhancesbig dataand viceversa

4 Leveraging bigdata with IBMInfoSphere MDM

5 Unlock the value ofbig data

6 Why InfoSphere?

Introduction

Business leaders are eager to harnessthe power of big data. However, as theopportunity increases, ensuring that sourceinformation is trustworthy and protectedbecomes exponentially more difcult. If notaddressed directly, end users may losecondence in the insights generated fromtheir datawhich can result in a failure toact on opportunities or against threats.

Information integration and governancemust be implemented within big dataapplications, providing appropriategovernance and rapid integration fromthe start. By automating informationintegration and governance and employingit at the point of data creation, organizationscan boost condence in big data.

A solid information integration andgovernance program must become anatural part of big data projects, supportingautomated discovery, proling andunderstanding of diverse data sets toprovide context and enable employeesto make informed decisions. It must beagile to accommodate a wide variety ofdata and seamlessly integrate with diversetechnologies, from data marts to ApacheHadoop systems. And it must automaticallydiscover, protect and monitor sensitive

information as part of big data applications.

1 Introduction

To make the most of big data, you have to start with data you trust. But the sheervolume and complexity of big data means that the traditional manual methodsof discovering, governing and correcting information are no longer feasible.


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6 Why InfoSphere?

When asked to rank their top three prioritiesfor big data, nearly half of the respondents ina 2012 IBM Institute for Business Value study 1 named customer-centric objectives such asimproving customer satisfaction and reducingchurn as the top concern. This is not a new

trend; understanding todays empoweredconsumer was also identied as a highpriority in both the 2011 IBM Global ChiefMarketing Ofcer Study 2 and the 2012 IBMGlobal Chief Executive Ofcer Study. 3

Companies clearly see big data as one keyto understanding and predicting customerbehavior. Transaction details, multichannelinteractions, social media, syndicated datafrom sources such as loyalty cards, and othercustomer-related information are powerfulnew tools for creating a complete pictureof customers preferences and demands.

The importance of understanding your customers

Big data objectives

Customer-centricoutcomes

Otherfunctionalobjectives

Top functional objectives identied by organizations with active big data pilots or implementations.Responses have been weighted and aggregated.

Customer-centric outcomes

Operational optimization

Risk/nancial management

49%15%

4%

14%

18%

New business model

Employee collaboration

Source: IBM Institute for Business Value . Analytics: The Real-World Use of Big Data.

Total respondents n = 1,061

Top functional objectives identied by organizations with active bigdata pilots or implementations.

Armed with insights from bigdata, business leaders can make

smarter decisions that improve the

customer experience.

2 The importance ofunderstanding yourcustomers
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6 Why InfoSphere?

In another IBM Institute for Business Value study, 4 CIOs identied the top vecapabilities that enable translation of datainto meaningful and useful intelligence:

1. Master data management (MDM)2. Client analytics3. Warehousing4. Dashboards5. Search

Executives expect these activities to leadto insights from both structured andunstructured data, and to enable them tounderstand customers at a level that ismore individual, responsive and profound.Indeed, efforts to improve the customerexperience should always start by knowingyour customer.

Top ve activities to turn data into intelligence

Low priority Some priority High priority

Master data management

Client analytics

Data warehousing

Visual dashboards

Search capabilities

2011 CIO Study Q17: What activities will you prioritize over the next 3 to 5 years to turn data into actionable information for your organization?(n=3,018); some responses may add to over 100% due to rounding up.

Source: IBM Institute of Business Value . Connect more: Intersecting insights from the IBM Global CEO, CMO and CIO Studies.

14% 68%19%

66%21%14%

64%24%12%

64%24%12%

59%27%14%

CIOs prioritize these ve activities to turn data into intelligence.

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6 Why InfoSphere?

More data, more understanding?Not necessarilyGaining a full understanding and a singleview of each customerwhat makes themtick, why they buy, how they prefer to shop,why they switch, what theyll buy next,what factors lead them to recommend acompany to othersis strategic for virtuallyevery company.

Unfortunately, many organizations lack thisunderstanding because they arent takingadvantage of traditional data sources. And ifyou dont know the customer based on thedata you have now, its going to be difcultto do so when you add big data to the mix.

The complexity of using big data to gainadditional insight boils down to severalspecic challenges:

Big data extends beyond on-premiseapplications and data warehouses it may include, for example, tweets in whichyour customers discuss your products.Because of this, it can be harder to collectand analyze than traditional structureddata. Potential insights can be buried inunstructured documents, spreadsheets,reports, email and social media.

The sheer volume of data aboutcustomers and products continues toskyrocket as well. Exploiting this datato develop a full understanding of yourcustomer can be daunting. How do youresolve hundreds of millions of records tosatisfy the need for real-time insight oranalyze large volumes of stored data todiscover patterns and insights?



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6 Why InfoSphere?

To complicate the situation further, customerdata is often captured in high-velocitystreams. Without real-time analysiscapabilities, important insights may be lost.

CIOs are likely to nd an MDM problemat the root of their big data challenges.Information about customers and productsis concealed within big data, and it must belinked to existing customer information thatis already stored in traditional structuredapplicationsotherwise, the new insightsextracted from big data risk becoming yetanother information silo.

MDM plays a crucial role in translating datainto meaningful and useful intelligence.

Together, big data and MDM can help CIOsand CMOs understand customers at anindividual level and become more responsiveto their needs. MDM creates context for bigdata by providing trusted information abouthow incoming unstructured data ts into thebusiness environment. MDM can also helpidentify and structure big data that will beused in controlled environments. Conversely,big data creates context for MDM by

providing new insights from social mediaand other sources, which helps companiesbuild richer customer proles.



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6 Why InfoSphere?

Analyzing big data in motionWith certain kinds of data, there is no timeto store it before acting on it because it isconstantly changing. This data is often (butnot always) generated by a sensor or otherinstrument. Speed matters for use casessuch as fraud detection, emergent healthcare and public safety, where gaining insightinto data in real timeand determiningwhether to act according to predenedrulescan be critical.

High-velocity, high-volume data calls forin-motion analytics. Streaming data mayarrive linked to a master data identier(a phone number, for example). In aneonatal ICU, sensor data is clearlymatched to a particular infant. Financial

transactions are unambiguously matchedto a credit card or Social Security number.However, not every piece of data thatstreams in is valuablewhich is why itshould be analyzed by a tool such as IBMInfoSphere Streams before being joined tomaster data.

InfoSphere Streams analyzes large datavolumes with micro-latency. Rather thanaccumulating and storing data rst, thesoftware analyzes data as it ows in andidenties conditions that trigger alerts (suchas outlier transactions that a bank ags aspotentially fraudulent during the credit cardauthorization process). When this occurs,the data is passed out of the stream andmatched with master data for better

business outcomes. InfoSphere Streamsgenerates a summary of the insights derivedfrom the stream analysis and matches itwith trusted information, such as customerrecords, to augment the master data.



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6 Why InfoSphere?

Analyzing a variety of big dataPerhaps the most widely recognized usecase for big data involves analyzing socialmedia or other highly variable data. If50 percent of big data initiatives focus onimproving the customer experience, 5 theseorganizations must:

1) Improve the 360-degree view of thecustomer by adding in additional insightfrom social media

2) Discover additional relationship links

based on insights from documents andunstructured text

3) Augment t raditional product informationwith dynamically derived producttraits based on web and socialmedia feedback

MDM helps improve holistic views of thecustomer by enhancing master datawith unstructured content. For example,Facebook postings may tell a hotel chain

that a high proportion of its business guestshave too many children for standard rewardrooms. The hotel can then respond byproviding reward privileges on larger suitesfor these high-value customers, potentiallyincreasing their loyalty.

In addition, MDM helps organizationsuncover relationship links that enhanceexisting knowledge about customersand open up opportunities to gain new

business or save costs. For example, byestablishing that two separate customersare part of the same household, thecompany can avoid sending duplicatemailings to the same address.



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6 Why InfoSphere?

Leveraging big data with IBM InfoSphere MDM

Few organizations, if any, will ever processand analyze all the big data they accumulate;it will likely continue to grow and diversifyahead of attempts to map and analyze it.However, by identifying, classifying andusing as much of the data as possible,organizations can gain valuable insights thatwould otherwise go undiscovered. IBM isfocused on several important areas to helporganizations make effective use of big dataand improve the customer experience:

Enhancing the 360-degree viewOnce an organization extracts insights frombig data, it must link that new information toexisting customer information in traditionalstructured applications.

Customer search

Transaction history fromMDM or ERP systems

Products owned bythe customer fromMDM system

Customer informationfrom MDM system

Unstructured internalinformation related tocustomer

Third-party informationrelated to customer

Watson Explorer pulls in master data information to populate certain areas, presenting it along with information from other sources to create acomplete view of an entity, such as a customer.



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6 Why InfoSphere?

Watson Explorer is a platform for navigating

and ltering large amounts of data in nearlyany format. It offers a visual dashboard that

joins master data about an individual withthat persons detailed transaction historyfrom emails, social media, packagedapplications and more. This data can helpsales staffparticularly inbound andoutbound marketersconnect withprospects on a more individual level and

achieve better sales rates.

Designed to handle both social and sensor-generated data, Watson Explorer lets usersspecify an axis and then searches for

matching data that it can fuse onto existing

master data. With master data as the startingpoint, Watson Explorer can also placereal-time activity streams into contextfor analysis.

Intelligence, law enforcement and fraudanalysts need to leverage all relevantpieces of information to connect thedots and make well-informed decisions

instantly. Watson Explorer works fromknown master data to give intelligenceagencies a unied view of all theirinformation sources to deliver visibility,transparency and insight.



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6 Why InfoSphere?

Investigators want to know everything abouta person from all sources, both structuredand unstructured. After investigators establisha master record, they may later uncoverlinks to that master record from documents,emails, blogs and so on. Since thesesources tend to be less trustworthy thandata from internal systems such as CRM or

ERP, the master record is not updated withthis information, but instead is correlated soan investigator can quickly see relationshipsand connections between their structuredrecord and unstructured content.

Another use case where big data canenhance master data involves productfeedback. Using web and social mediadata, organizations can augment traditionalproduct information to better understandcustomer sentiment. The resulting insightscan help an organization update marketingoffers, address technical issues, adjustproduct specs or change packaging.

The company could also update orenhance the master record with insightfrom unstructured text.

One hospitality and travelorganization used the bulk

comparison process to match andconsolidate 1 billion source recordsdown to 250 million unique partiesin just 22 hours.

Mastering information at bigdata scaleEnterprises are being ooded with data ofall types, easily amassing terabytes andeven petabytes of information as datavolumes grow. Thats why IBM designedInfoSphere MDM to meet the mostdemanding performance requirements.

InfoSphere MDM performs well not only inreal-time transaction processing, but alsoin the initial bulk comparison and loadprocess needed to establish the MDM hub.

The bulk comparison and load processis scalable across processors and evenacross servers to help get the hub up andrunning quickly.



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6 Why InfoSphere?

However, challenges can and do exist withthe inherent limitations of entity resolutionwithin relational databases. Identities mustbe compared against potentially all availableentities, and to do this, comparison datamust be retrieved from disk for processing.IBM InfoSphere BigInsightsbased onHadoopoffers a solution for processingthis data with near-limitless capacity thatscales with CPU capacity.

Scalability and performance in resolvingmaster data records can become an issueparticularly when loading large lead lists andmatching that list with known customersand prospects, or when matching recordsagainst large watch lists to combat threatand fraud. Analyzing large volumes ofstored data enables organizations to

discover previously hidden patterns andinsights that allow them to optimizeprocesses and protability.

For example, one healthcare insuranceprovider analyzed more than 400 millionclaims, looking for potentially deadly druginteractions. Text from patient phone callsis intricately nested and often mentionsmedications obtained elsewhere or withouta claim form. A standard drug name mightbe Diucan, but it could be called Flucanol,Batacan or yeast medicine in a transcript.

The companys internal system took morethan 100 hours to perform this analysis.

However, when the company began usingInfoSphere BigInsights to match transcriptsto master data records, analysis time

dropped to just 10 hours for more than6 TB of data. 6 InfoSphere BigInsights alsoexcels at extracting structured data fromtext analysis and matching it to masterdata. These capabilities pave the way fora new class of applications for customerexperience analysis, social media analysisand fraud detection.


IBM has conducted performancebenchmark tests in large deployments

(over 100 million party records) andalso has fully referenceable customerimplementations of over 100 million

party records scaling to hundreds oftransactions per second.


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6 Why InfoSphere?

Unlock the value of big data

InfoSphere MDM is a complete, exible andproven MDM solution that creates trustedviews to improve operational businessprocesses, big data and analytics. Part ofthe InfoSphere platform, it supports alldomains, architectural styles and use casesacross all industries, and offers quick time-to-value through pre-built and customizabledata models and business services.

Any enterprise that accumulates largevolumes of fast-moving structured andunstructured data needs a well-denedapproach to store, analyze and leverageit, as well as gauge its trustworthiness.InfoSphere MDM and IBM big data toolsare keys that unlock the value of big data.

They help companies analyze big data ina timely manner, match it to master dataand derive actionable information to informdecisions and extract insights. InfoSphereMDM complements big-data analytics

tools and helps organizations deliver trustedinformation to win sales, satisfy andretain customers, improve operationsand increase compliance.



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6 Why InfoSphere?

Why InfoSphere?

As a critical element of Watson Foundations,the IBM big data and analytics platform,InfoSphere Information Integration andGovernance (IIG) provides optimal scalabilityand performance for massive data volumes,agile and rightsized integration andgovernance for the increasing velocity ofdata, and support and protection for a widevariety of data types and big data systems.InfoSphere helps make big data andanalytics projects successful by deliveringbusiness users the condence to act oninsight. InfoSphere capabilities include:

Metadata, business glossary andpolicy management: Dene metadata,business terminology and governancepolicies with IBM InfoSphere BusinessInformation Exchange.

Data integration: Handle all integrationrequirements, including batch datatransformation and movement (IBMInfoSphere Information Server), real-timereplication (IBM InfoSphere Data Replication)and data federation (IBM InfoSphereFederation Server).

Data quality: Parse, standardize, validateand match enterprise data with IBMInfoSphere Information Server forData Quality.

Master data management: Act on atrusted view of your customers, products,suppliers, locations and accounts withInfoSphere MDM.

Data lifecycle management: Managethe data lifecycle from test data creationthrough retirement and archiving with IBMInfoSphere Optim.

Data security and privacy: Continuouslymonitor data access and protectrepositories from data breaches, andsupport compliance with IBM InfoSphereGuardium. Mask and help protectsensitive data with InfoSphere Optim.

Additional resources To learn more about the IBM approach toinformation integration, MDM andgovernance for big data, please contactyour IBM representative or IBM BusinessPartner, or visit:

ibm.com /software/data/information-integration-governance

ibm.com /software/products/us/en/ infosphere-master-data-management

6 Why InfoSphere?
http://ibm.com/software/data/information-integration-governancehttp://ibm.com/software/data/information-integration-governancehttp://ibm.com/software/data/information-integration-governancehttp://ibm.com/software/data/master-data-managementhttp://ibm.com/software/data/master-data-managementhttp://ibm.com/software/data/master-data-managementhttp://ibm.com/software/data/master-data-managementhttp://ibm.com/software/data/master-data-managementhttp://ibm.com/software/data/information-integration-governancehttp://ibm.com/software/data/information-integration-governance


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IMM14124-USEN-01

Copyright IBM Corporation 2014IBM CorporationSoftware GroupRoute 100Somers, NY 10589

Produced in the United States of America March 2014

IBM, the IBM logo, ibm.com, BigInsights, Guardium, IBM Watson, InfoSphere, and Optim are trademarks of InternationalBusiness Machines Corp., registered in many jurisdictions

worldwide. Other product and service names might be trademarksof IBM or other companies. A current list of IBM trademarks is

available on the web at Copyright and trademark information atibm.com /legal/copytrade.shtml

This document is current as of the initial date of publication andmay be changed by IBM at any time. Not all offerings are availablein every country in which IBM op erates.

The performance data discussed herein is presented as derivedunder specic operating conditions. Actual results may vary.

THE INFORMATION IN THIS DOCUMENT IS PROVIDEDAS IS WITHOUT ANY WARRANTY, EXPRESS ORIMPLIED, INCLUDING WITHOUT ANY WARRANTIES OF

MERCHANTABILITY, FITNESS FOR A PARTICULARPURPOSE AND ANY WARRANTY OR CONDITION OFNON-INFRINGEMENT. IBM products are warranted accordingto the terms and conditions of the agreements under which theyare provided.

The client is responsible for ensuring compliance with laws andregulations applicable to it. IBM does not provide legal advice orrepresent or warrant that its services or products will ensure thatthe client is in compliance with any law or regulation.

Actual available storage capacity may be reported for bothuncompressed and compressed data and will vary and may be lessthan stated.

Please Recycle

1, 5 Analytics: The real-world use of big data. ibm.com /services/us/gbs/thoughtleadership/ibv-big-data-at-work.html2 2011 IBM Global Chief Marketing Ofcer Study. ibm.com /services/us/cmo/cmostudy2011/cmo-registration.html 3 2012 IBM Global Chief Executive Ofcer Study. ibm.com /services/us/en/c-suite/ceostudy20124 IBM Institute of Business Value. Connect more: Intersecting insights from the IBM Global CEO, CMO and CIO Studies. ibm.com /services/

us/gbs/thoughtleadership/connectmore6 DeRoos, Dirk; Deutsch, Tom; Eaton, Chris; Lapis, George; Zikopoulous, Paul. Understanding Big Data: Analytics for Enterprise Class Hadoop

and Streaming Data. McGraw-Hill, 2012. ibm.com /common/ssi/cgi-bin/ssialias?subtype=WH&infotype=SA&appname=SWGE_IM_DD_USEN&htmld=IML14297USEN&attachment=IML14297USEN.PDF
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